Fiscal Incentives for CCUS within Alberta’s Oil Sands Royalty Regime
Bibliographic record
Abstract
Carbon Capture, Utilization and Storage (CCUS) technology is both a costly endeavor and one with the potential to dramatically reduce carbon dioxide (CO2) emissions. The Canadian and Albertan governments recognize that CCUS technology is key to reducing carbon emissions in Alberta’s oil and gas sector. Alberta’s oil sands have high emissions at localized sources, suggesting that CCUS technology could be viable despite its high costs, but government funding is still required to support firms in this investment. This paper models the current fiscal incentives for the lifetime costs of a CCUS facility added to an in situ oil sands extraction site. The model indicates that Alberta's policy incentives and royalty system cover 35.6% of the funding for the lifetime cost of a CCUS facility. This includes 12.8% from the federal Investment Tax Credit, 3.5% from the Alberta Carbon Capture Incentive Program, and 20.3%. If a firm decides to forgo the current policy incentives and rely only on oil sands royalty offsets, it can expect that reduced royalty payments will cover 46% of the project. However, there is a more attractive option than relying on royalty cost offsets. This is because royalties come in throughout the project's life. In contrast, current policy incentives provide funding upfront. This model reveals that early provincial funding is lacking and incongruent with historic provincial incentives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".